MLA-C01 · Question #167
A company is training a new ML model to replace a model that is deployed on an Amazon SageMaker AI real-time endpoint. An ML engineer needs to determine the latency and the accuracy of the new…
The correct answer is D. Perform shadow testing with a traffic sampling percentage of 100%. Shadow testing sends a copy of production traffic to the new model while keeping all user-facing responses served by the existing model, allowing accurate measurement of latency and accuracy in real production conditions without impacting users.
Question
A company is training a new ML model to replace a model that is deployed on an Amazon SageMaker AI real-time endpoint. An ML engineer needs to determine the latency and the accuracy of the new model. The ML engineer must evaluate the new model in a production scenario without affecting the users of the existing model. Which solution will meet these requirements?
Options
- APerform a blue/green deployment with linear traffic shifting.
- BPerform a blue/green deployment with canary traffic shifting.
- CPerform a rolling deployment with a rolling batch size of 50% of the current fleet.
- DPerform shadow testing with a traffic sampling percentage of 100%.
How the community answered
(36 responses)- A8% (3)
- B3% (1)
- C6% (2)
- D83% (30)
Explanation
Shadow testing sends a copy of production traffic to the new model while keeping all user-facing responses served by the existing model, allowing accurate measurement of latency and accuracy in real production conditions without impacting users.
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